White Papers

Rethinking Hierarchical Text Classification: Insights from Multi-Agent Experiments with Small Language Models

Intelligent Case Classification

This white paper presents Intelligent Case Classification, an advanced Natural Language Processing and Machine Learning-driven text classification approach for analyzing Dell customer support interactions.

Simplifying AI: Dell Enterprise Hub Enables Kubernetes Deployment for AI Models

This document describes a solution for deploying AI models on Kubernetes using code from the Dell Enterprise Hub on Hugging Face.

Dell Automotive Reference Architecture

The Dell Automotive Reference Architecture defines a starter kit of clustered servers, storage, and software applications optimized for AI model development and verification for SAE levels L2 to L5 autonomous vehicle programs targeted to automotive companies.

Scale AI Training and Fine-Tuning with Dell PowerScale and PowerEdge Servers

This white paper discusses the benefits of the Dell PowerScale scale-out file platform for AI, describing a Generative AI solution architecture combining NVIDIA AI Enterprise and NEMO with Dell storage, server, and network infrastructure.

Llama 2: Inferencing on a Single GPU

This document describes how to deploy and run inferencing on a Meta Llama 2 7B parameter model using a single NVIDIA A100 GPU with 40GB memory.

AI Made Easy: Unleash the Potential of Dell Enterprise Hub on Hugging Face

This document describes the advantages and features of using the Dell Enterprise Portal on Hugging Face to select and deploy optimized self-contained containers on validated Dell infrastructure.

Training Models Made Easy with Dell Enterprise Hub

This paper describes a Dell Reference Design for Dell Enterprise Hub’s new support for training models. The paper outlines the benefits of this approach for an enterprise and provides implementation guidance.

Dell Managed Service for Machine Learning Operations – Security Overview

This paper describes the shared security responsibilities between Dell Technologies and the customer. Dell Technologies has designed and implemented internal security controls, including suggested best practices and recommendations, to guide customers who use

Smart Security Enhancer as a Device Guardian to Mitigate Software Vulnerabilities

This white paper describes a new approach called Smart Security Enhancer (SSE), an in-house solution that keeps customers’ devices at a safe state. It discusses how SSE proactively identifies and prioritizes security updates to reduce software vulnerabilities.

Demand Forecasting: A Machine Learning-Based Solution to Forecast Demand Signals for Dell Services

This white paper describes the features and benefits of a machine learning-based solution that predicts and estimates the future demand of various demand signals, which in turn assists several Dell teams to make better business decisions.

Dell and Comet - Experiment Management for Machine Learning Projects

A two-part white paper covering the benefits of using the Comet machine learning platform with a Dell EMC AI-enabled infrastructure.

Harness the Power of Dell EMC PowerEdge R7525/R7515 Servers with NVIDIA GPUs for Machine Learning Inference

Dell Technologies has a rich portfolio of hardware platforms that are being used to power ML and DL solutions. This paper showcases two servers from Dell Technologies that are powered by AMD processors and accelerated by NVIDIA GPUs.

Edge Simulation for Advanced Development Systems

This whitepaper describes how Dell Technologies is working with monoDrive to produce high-fidelity simulations using the industry-leading PowerEdge and PowerStore systems.

MLPerf™ Inference v1.0 – NVIDIA GPU-Based Benchmarks on Dell EMC PowerEdge R750xa Servers

This paper describes Dell EMC PowerEdge R750xa server performance results submitted to the MLPerf™ Inference v1.0 benchmark. The results indicate that the server delivered optimal performance making it an excellent choice for inference workloads.

Dell Technologies DellNLP: A Centralized Text Processing Engine

This white paper describes how an in-house Natural Language Processing (NLP) product, DellNLP, leverages machine learning and artificial intelligence to derive insights from text data within Support Services and improves the customer experience.

White Paper—Optimize Machine Learning Through MLOps with Dell Technologies and cnvrg.io

This white paper defines MLOps, describes its purpose and increasing relevance for practitioners of machine learning and artificial intelligence, and details how organizations can use MLOps to optimize their machine learning deployments and bring prototypes in

Automate Machine Learning with H2O Driverless AI on Dell Infrastructure

This technical white paper discusses the benefits of automated machine learning and the challenges of non-automated model development that it overcomes. The paper presents an overview of the H2O Driverless AI product from H2O.ai, along with a solution architec

Performance of Dell Servers Running with NVIDIA Accelerators on MLPerf™ Training v2.0

This white paper describes the successful submission, which is the sixth round of submission, to MLPerf Training v2.0 by Dell Technologies. It provides an overview and highlights the performance of different servers that were in the submission.

DELLQUINN: A Machine Learning-Based, Resource Capacity Optimization Solution

This white paper describes a machine learning-based resource optimization solution that helps the Dell High End Storage Field Services Team perform capacity planning more efficiently and with the potential to optimize resource utilization and overtime.

Empowering Support Agents with Next Best Action Recommendations

This white paper describes how the Next Best Action Recommendation solution helps Dell support agents troubleshoot technical issues more effectively for the Infrastructure Support Group (ISG).

MLPerf™ Inference v2.1 with NVIDIA GPU-Based Benchmarks on Dell PowerEdge Servers

This white paper describes the successful submission, which is the sixth round of submissions to MLPerf Inference v2.1 by Dell Technologies. It provides an overview and highlights the performance of different servers that were in submission.

Accelerating End-to-End Machine Learning Using the Intel® oneAPI Toolkit and Intel® Flex Series GPU

This white paper describes the accelerated performance of an E2E ML pipeline using Intel’s oneAPI AI Analytics Toolkit as compared to a baseline pandas implementation.

Memory Consumption Modeling of Deep Learning Workloads

In this white paper, we present a multi-parameter modeling approach to generate analytical models that accurately predict the peak memory consumption of a Deep Learning workload.

Conversational AI with Kore.ai

This technical white paper discusses the market trends, use cases, and benefits of Conversational AI. It describes a solution and validated reference architecture for Conversational AI with the Kore.ai Experience Optimization Platform on Dell infrastructure.

White Paper - Virtualizing GPUs for AI with VMware and NVIDIA Based on Dell Infrastructure

This white paper describes Dell Technologies Validated Design for AI, which was designed in collaboration with VMware and NVIDIA. It details how enterprises can run AI workloads alongside existing applications in data centers without compromising performance.

GPU Optimization with Run:ai Atlas

This technical white paper describes a solution and validated reference architecture for GPU resource allocation and scheduling using the Run:ai Atlas platform on Dell infrastructure.

White Paper—Dell Server Performance Using the MLPerf™ Training v3.0 Benchmark

This white paper describes the performance of the latest generation of Dell servers with NVIDIA A100 and H100 GPUs for training deep learning workloads using the MLPerf Training v3.0 benchmark. It provides a comprehensive single-node analysis of MLPerf Trainin

Digital Assistant with Red Hat OpenShift AI on Dell APEX Cloud Platform for Red Hat OpenShift

This white paper provides a summary for using Red Hat OpenShift AI on Dell APEX Cloud Platform to create a digital assistant. This solution leverages a LLM and the RAG technique in combination with a set of vectorized documents.

Accelerating ML and DL Pipelines with Intel® Granulate™ and Dell PowerEdge Servers on Cloudera Data Platform

This white paper describes a solution for empowering enterprises and digital native businesses with real-time, continuous application-level performance optimization and capacity management, on any type of workload.

An AI Language Translation Project ─ AI Translations: Machine and Creature Languages

This white paper provides example uses of Machine Learning language models for language translations in the context of a storyline application using a Dell Technologies server and an NVIDIA GPU in a container environment.

AI Driven Speech Recognition and Synthesis on Dell APEX Cloud Platform for Red Hat OpenShift

This document introduces a solution that achieves automated speech recognition (ASR) and text-to-speech (TTS) capabilities using NVIDIA GPUs on the Dell APEX Cloud Platform for Red Hat OpenShift, with Dell software-defined storage and Dell switches.

MLPerf Inference v3.1: Results using Dell Servers with Qualcomm Cloud AI 100 Accelerators

The MLPerf Inference v3.1 results are out and Dell Technologies and Qualcomm Technologies, Inc. have again produced impressive inference results on a breadth of models, this time using 2x Qualcomm® Cloud AI 100 Standard accelerators on a Dell XR4520c server.

Using Machine Learning to Recommend Service Product Offerings

This white paper describes a new product called Dell Technologies Service Product Recommender, an in-house solution that provides service recommendations while customers are shopping for new devices, selecting new contracts, or upgrading existing contracts.

Accelerate AI with Dell APEX Cloud Platform for Microsoft Azure and Microsoft Azure AI Services

This white paper demonstrates running Microsoft Azure AI Services as connected on-premises containers on the Dell APEX Cloud Platform for Azure. A healthcare use case is used as an example utilizing transcription, vocalization, and translation services.

Model Customization for Code Creation with Red Hat OpenShift AI on Dell AI Optimized Infrastructure

How training and tuning an AI model can help your enterprise’s evolution to better code